Targeted Capture and Massively Parallel Sequencing of 12 Human Exomes

Total Page:16

File Type:pdf, Size:1020Kb

Targeted Capture and Massively Parallel Sequencing of 12 Human Exomes Vol 461 | 10 September 2009 | doi:10.1038/nature08250 LETTERS Targeted capture and massively parallel sequencing of 12 human exomes Sarah B. Ng1, Emily H. Turner1, Peggy D. Robertson1, Steven D. Flygare1, Abigail W. Bigham2, Choli Lee1, Tristan Shaffer1, Michelle Wong1, Arindam Bhattacharjee4, Evan E. Eichler1,3, Michael Bamshad2, Deborah A. Nickerson1 & Jay Shendure1 Genome-wide association studies suggest that common genetic mutations in MYH3 (ref. 5). Unpaired, 76 base-pair (bp) reads12 variants explain only a modest fraction of heritable risk for com- from post-enrichment shotgun libraries were aligned to the reference mon diseases, raising the question of whether rare variants account genome13. On average, 6.4 gigabases (Gb) of mappable sequence was for a significant fraction of unexplained heritability1,2. Although generated per individual (20-fold less than whole genome sequencing DNA sequencing costs have fallen markedly3,theyremainfarfrom with the same platform12), and 49% of reads mapped to targets what is necessary for rare and novel variants to be routinely iden- (Supplementary Table 1). After removing duplicate reads that tified at a genome-wide scale in large cohorts. We have therefore represent potential polymerase chain reaction artefacts14, the average sought to develop second-generation methods for targeted sequen- fold-coverage of each exome was 513 (Supplementary Fig. 1). On cing of all protein-coding regions (‘exomes’), to reduce costs while average per exome, 99.7% of targeted bases were covered at least enriching for discovery of highly penetrant variants. Here we report once, and 96.3% (25.6 Mb) were covered sufficiently for variant call- on the targeted capture and massively parallel sequencing of the ing ($83 coverage and Phred-like15 consensus quality $30). This exomes of 12 humans. These include eight HapMap individuals corresponded to 78% of genes having .95% of their coding bases representing three populations4, and four unrelated individuals called (Supplementary Fig. 2 and Supplementary Data 2). The aver- with a rare dominantly inherited disorder, Freeman–Sheldon syn- age pairwise correlation coefficient between individuals for gene-by- drome (FSS)5. We demonstrate the sensitive and specific identifica- gene coverage was 0.87, consistent with systematic bias in coverage tion of rare and common variants in over 300 megabases of coding between individual exomes. sequence. Using FSS as a proof-of-concept, we show that candidate False positives and false negatives are critical issues in genomic genes for Mendelian disorders can be identified by exome sequen- resequencing. We assessed the quality of our exome data in four ways. cing of a small number of unrelated, affected individuals. This First, comparing sequence-based calls for the eight HapMap exomes strategy may be extendable to diseases with more complex genetics to array-based genotyping, we observed a high concordance with through larger sample sizes and appropriate weighting of non- both homozygous (99.94%; n 5 219,077) and heterozygous synonymous variants by predicted functional impact. (99.57%; n 5 43,070) genotypes (Table 1). Second, we compared Protein-coding regions constitute ,1% of the human genome or our coding single-nucleotide polymorphism (cSNP) catalogue to ,30 megabases (Mb), split across ,180,000 exons. A brute-force ,1 Mb of coding sequence determined in each of the eight approach to exome sequencing with conventional technology6 is HapMap individuals by molecular inversion probe (MIP) capture expensive relative to what may be possible with second-generation and direct resequencing16. At coordinates called in both data sets, platforms3. However, the efficient isolation of this fragmentary geno- 99.9% of all cSNPs (n 5 4,620) and 100% of novel cSNPs mic subset is technically challenging7. The enrichment of an exome (n 5 334) identified here were concordant, consistent with a low false by hybridization of shotgun libraries constructed from 140 mgof discovery rate. Third, we compared the NA18507 cSNPs identified genomic DNA to seven microarrays was described previously8.To here to those called by recent whole genome sequencing of this improve the practicality of hybridization capture, we developed a individual12, and found substantial overlap (Supplementary Fig. 3). protocol to enrich for coding sequences at a genome-wide scale start- The relative numbers of cSNPs called by only one approach, and the ing with 10 mg of DNA and using two microarrays. Our initial target proportions of these represented in dbSNP, indicate that exome was 27.9 Mb of coding sequence defined by CCDS (the NCBI sequencing has equivalent sensitivity for cSNP detection compared Consensus Coding Sequence database)9. This curated set avoids the to whole genome sequencing. Fourth, we compared our data to inclusion of spurious hypothetical genes that contaminate broader cSNPs in high-quality Sanger sequence of single haplotype regions exome definitions10. The target is reduced to 26.6 Mb on exclusion of from fosmid clones of the same HapMap individuals17. Most fosmid- regions that are poorly mapped with our anticipated read length defined cSNPs (38 of 40) were at coordinates with sufficient coverage owing to paralogous sequences elsewhere in the genome in our data for variant calling. Of these, 38 of 38 were correctly (Supplementary Data 1). identified as variant. We captured and sequenced the exomes of eight individuals previ- A comparison of our data to past reports on exonic18 or exomic8 ously characterized by the HapMap4 and Human Genome Structural array-based capture revealed roughly equivalent capture specificity, Variation11 projects. We also analysed four unrelated individuals but greater completeness in terms of coverage and variant calling affected with Freeman–Sheldon syndrome (FSS; Online Mendelian (Supplementary Table 2). These improvements probably arise from Inheritance in Man (OMIM) #193700), also called distal arthro- a combination of greater sequencing depth and differences in array gryposis type 2A, a rare autosomal dominant disorder caused by designs and in experimental conditions for capture. Within the set of 1Department of Genome Sciences, 2Department of Pediatrics, University of Washington, 3Howard Hughes Medical Institute, Seattle, Washington 98195, USA. 4Agilent Technologies, Santa Clara, California 95051, USA. 272 ©2009 Macmillan Publishers Limited. All rights reserved NATURE | Vol 461 | 10 September 2009 LETTERS Table 1 | Sequence coverage and array-based validation Concordance with Illumina Human1M-Duo calls Individual Covered $13 Sequence called Homozygous reference Heterozygous Homozygous non-reference NA18507 (YRI) 26,477,161 (99.7%) 25,795,189 (97.1%) 23757/23762 (99.98%) 5553/5583 (99.46%) 3582/3592 (99.72%) NA18517 (YRI) 26,476,761 (99.7%) 25,748,289 (97.0%) 23701/23705 (99.98%) 5575/5601 (99.54%) 3568/3579 (99.69%) NA19129 (YRI) 26,491,035 (99.8%) 25,733,587 (96.9%) 23701/23708 (99.97%) 5482/5510 (99.49%) 3681/3690 (99.76%) NA19240 (YRI) 26,486,481 (99.7%) 25,576,517 (96.3%) 23546/23551 (99.98%) 5600/5634 (99.40%) 3542/3549 (99.80%) NA18555 (CHB) 26,475,665 (99.7%) 25,529,861 (96.1%) 23980/23984 (99.98%) 4877/4893 (99.67%) 3776/3786 (99.74%) NA18956 (JPT) 26,454,942 (99.6%) 25,683,248 (96.7%) 24217/24221 (99.98%) 4890/4910 (99.59%) 3751/3760 (99.76%) NA12156 (CEU) 26,476,155 (99.7%) 25,360,704 (95.5%) 23789/23794 (99.98%) 5493/5514 (99.62%) 3206/3213 (99.78%) NA12878 (CEU) 26,439,953 (99.6%) 25,399,572 (95.6%) 23885/23891 (99.97%) 5413/5425 (99.78%) 3274/3292 (99.45%) FSS10066 (Eur) 26,467,140 (99.7%) 25,546,738 (96.2%) NA NA NA FSS10208 (Eur) 26,461,768 (99.6%) 25,576,256 (96.3%) NA NA NA FSS22194 (Eur) 26,426,401 (99.5%) 25,454,551 (95.9%) NA NA NA FSS24895 (Eur) 26,478,775 (99.7%) 25,602,677 (96.4%) NA NA NA The number of coding bases covered at least 13 and with sufficient coverage to variant call ($83 and consensus quality $30) are listed for each exome, with the fraction of the aggregate target (26.6 Mb) that this represents in parentheses. For the eight HapMap individuals, concordance with array genotyping (Illumina Human1M-Duo) is listed for positions that are homozygous for the reference allele, heterozygous or homozygous for the non-reference allele (according to the array genotype). CEU, CEPH HapMap; CHB, Chinese HapMap; Eur, European–American ancestry (non- HapMap); JPT, Japanese HapMap; YRI, Yoruba HapMap. NA, Not applicable. called positions, the high concordance with heterozygous array- (n 5 47,079), 32% of annotated variants and 86% of novel variants based genotypes (.99%) provides an estimate of our sensitivity for were singleton observations across 24 chromosomes (Fig. 1a). rare variant detection, as rare variants are overwhelmingly expected We also estimated the total number of cSNPs in each individual to be heterozygous. However, sensitivity was limited in that ,4% of relative to the reference genome (Table 2b). As the precise and com- known heterozygous genotypes were at coordinates where there was prehensive definition of the human exome remains incomplete, we insufficient coverage to make a confident call. extrapolated our data to an estimated exome size of exactly 30 Mb. There were 56,240 cSNPs called in one or more individuals, of The results were remarkably consistent by population. As expected, a which 13,347 were novel. On average, 17,272 cSNPs were called per higher number of non-synonymous cSNPs were estimated for the individual, of which 92% were already annotated in a public database Yoruba individuals (average 10,254; n 5 4) than non-Africans (aver- (dbSNP v129) (Table 2a). The proportion of previously annotated age 8,489; n 5 8). More heterozygous cSNPs were estimated for the cSNPs was consistent by population, and higher for European (94%; four Yoruba (average 14,995) than the six European Americans n 5 6) and Asian (93%; n 5 2) than Yoruba (88%; n 5 4) ancestry.
Recommended publications
  • Whole Exome and Whole Genome Sequencing – Oxford Clinical Policy
    UnitedHealthcare® Oxford Clinical Policy Whole Exome and Whole Genome Sequencing Policy Number: LABORATORY 024.11 T2 Effective Date: October 1, 2021 Instructions for Use Table of Contents Page Related Policies Coverage Rationale ....................................................................... 1 Chromosome Microarray Testing (Non-Oncology Documentation Requirements ...................................................... 2 Conditions) Definitions ...................................................................................... 2 Molecular Oncology Testing for Cancer Diagnosis, Prior Authorization Requirements ................................................ 3 Prognosis, and Treatment Decisions Applicable Codes .......................................................................... 3 • Preimplantation Genetic Testing Description of Services ................................................................. 4 Clinical Evidence ........................................................................... 4 U.S. Food and Drug Administration ........................................... 22 References ................................................................................... 22 Policy History/Revision Information ........................................... 26 Instructions for Use ..................................................................... 27 Coverage Rationale Whole Exome Sequencing (WES) Whole Exome Sequencing (WES) is proven and Medically Necessary for the following: • Diagnosing or evaluating a genetic disorder
    [Show full text]
  • Whole Exome Sequencing Faqs
    WHOLE EXOME SEQUENCING (WES) Whole Exome Sequencing (WES) is generally ordered when a patient’s medical history and physical exam strongly suggest that there is an underlying genetic etiology. In some cases, the patient may have had an extensive evaluation consisting of multiple genetic tests, without identifying an etiology. In other cases, a physician may opt to order one of the Whole Exome Sequencing tests early in the patient’s evaluation in an effort to expedite a possible diagnosis and reduce costs incurred by multiple tests. Whole Exome Sequencing is a highly complex test that is newly developed for the identification of changes in a patient’s DNA that are causative or related to their medical concerns. In contrast to current sequencing tests that analyze one gene or small groups of related genes at a time, Whole Exome Sequencing analyzes the exons or coding regions of thousands of genes simultaneously using next-generation sequencing techniques. The exome refers to the portion of the human genome that contains functionally important sequences of DNA that direct the body to make proteins essential for the body to function properly. These regions of DNA are referred to as exons. There are approximately 180,000 exons in the human genome which represents about 3% of the genome. These 180,000 exons are arranged in about 22,000 genes. It is known that many of the errors that occur in DNA sequences that then lead to genetic disorders are located in the exons. Therefore, sequencing of the exome is thought to be an efficient method of analyzing a patient’s DNA to discover the genetic cause of diseases or disabilities.
    [Show full text]
  • Next-Gen Sequencing Identifies Non-Coding Variation Disrupting
    OPEN Molecular Psychiatry (2018) 23, 1375–1384 www.nature.com/mp ORIGINAL ARTICLE Next-gen sequencing identifies non-coding variation disrupting miRNA-binding sites in neurological disorders P Devanna1, XS Chen2,JHo1,2, D Gajewski1, SD Smith3, A Gialluisi2,4, C Francks2,5, SE Fisher2,5, DF Newbury6,7 and SC Vernes1,5 Understanding the genetic factors underlying neurodevelopmental and neuropsychiatric disorders is a major challenge given their prevalence and potential severity for quality of life. While large-scale genomic screens have made major advances in this area, for many disorders the genetic underpinnings are complex and poorly understood. To date the field has focused predominantly on protein coding variation, but given the importance of tightly controlled gene expression for normal brain development and disorder, variation that affects non-coding regulatory regions of the genome is likely to play an important role in these phenotypes. Herein we show the importance of 3 prime untranslated region (3'UTR) non-coding regulatory variants across neurodevelopmental and neuropsychiatric disorders. We devised a pipeline for identifying and functionally validating putatively pathogenic variants from next generation sequencing (NGS) data. We applied this pipeline to a cohort of children with severe specific language impairment (SLI) and identified a functional, SLI-associated variant affecting gene regulation in cells and post-mortem human brain. This variant and the affected gene (ARHGEF39) represent new putative risk factors for SLI. Furthermore, we identified 3′UTR regulatory variants across autism, schizophrenia and bipolar disorder NGS cohorts demonstrating their impact on neurodevelopmental and neuropsychiatric disorders. Our findings show the importance of investigating non-coding regulatory variants when determining risk factors contributing to neurodevelopmental and neuropsychiatric disorders.
    [Show full text]
  • An Efficient and Scalable Analysis Framework for Variant Extraction and Refinement from Population-Scale DNA Sequence Data
    Downloaded from genome.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press Method An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data Goo Jun,1,2 Mary Kate Wing,2 Gonçalo R. Abecasis,2 and Hyun Min Kang2 1Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA; 2Center for Statistical Genetics and Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109, USA The analysis of next-generation sequencing data is computationally and statistically challenging because of the massive vol- ume of data and imperfect data quality. We present GotCloud, a pipeline for efficiently detecting and genotyping high- quality variants from large-scale sequencing data. GotCloud automates sequence alignment, sample-level quality control, variant calling, filtering of likely artifacts using machine-learning techniques, and genotype refinement using haplotype in- formation. The pipeline can process thousands of samples in parallel and requires less computational resources than current alternatives. Experiments with whole-genome and exome-targeted sequence data generated by the 1000 Genomes Project show that the pipeline provides effective filtering against false positive variants and high power to detect true variants. Our pipeline has already contributed to variant detection and genotyping in several large-scale sequencing projects, including the 1000 Genomes Project and the NHLBI Exome Sequencing Project. We hope it will now prove useful to many medical sequencing studies. [Supplemental material is available for this article.] The cost of human genome sequencing has declined rapidly, pow- There is a pressing need for software pipelines that support ered by advances in massively parallel sequencing technologies.
    [Show full text]
  • Whole Exome and Whole Genome Sequencing
    UnitedHealthcare® Community Plan Medical Policy Whole Exome and Whole Genome Sequencing Policy Number: CS150.J Effective Date: October 1, 2021 Instructions for Use Table of Contents Page Related Community Plan Policies Application ..................................................................................... 1 • Chromosome Microarray Testing (Non-Oncology Coverage Rationale ....................................................................... 1 Conditions) Definitions ...................................................................................... 2 • Molecular Oncology Testing for Cancer Diagnosis, Applicable Codes .......................................................................... 3 Prognosis, and Treatment Decisions Description of Services ................................................................. 4 • Preimplantation Genetic Testing Clinical Evidence ........................................................................... 4 U.S. Food and Drug Administration ........................................... 22 Commercial Policy References ................................................................................... 22 • Whole Exome and Whole Genome Sequencing Policy History/Revision Information ........................................... 26 Medicare Advantage Coverage Summaries Instructions for Use ..................................................................... 26 • Genetic Testing • Laboratory Tests and Services Application This Medical Policy does not apply to the states listed below; refer to
    [Show full text]
  • Clinical Exome Sequencing Tip Sheet – Medicare Item Numbers 73358/73359
    Clinical exome sequencing Tip sheet – Medicare item numbers 73358/73359 Glossary Chromosome microarray (CMA or molecular Monogenic conditions (as opposed karyotype): CMA has a Medicare item number to polygenic or multifactorial conditions) are for patients presenting with intellectual caused by variants in a single gene. Variants disability, developmental delay, autism, or at may be inherited (dominant or recessive least two congenital anomalies. CMA is the fashion), or may occur spontaneously (de recommended first line test in these cases as novo) showing no family history. it can exclude a chromosome cause of disease which is unlikely to be detected by Whole exome sequence – sequencing only exome. the protein coding genes (exons). The exome is ~2% of the genome and contains ~85% of Gene panel is a set of genes that are known to disease-causing gene variants. be associated with a phenotype or disorder. They help narrow down the search Whole genome sequence – sequencing the for variants of interest to genes with evidence entire genome (all genes, including coding linking them to particular phenotypes and noncoding regions) Human phenotype ontology (HPO) terms Singleton – Analysis of the child only. describe a phenotypic abnormality using a Trio – analysis of the child and both biological standard nomenclature. Ideally, all clinicians parents. and scientists are using the same terms. Variant - A change in the DNA code that Mendeliome refers to the ~5,000 genes (out of differs from a reference genome. about 20,000 protein coding genes) that are known to be associated with monogenic disease. As variants in new genes are identified with evidence linking them with human disease, they are added to the Mendeliome.
    [Show full text]
  • The Genomics Era: the Future of Genetics in Medicine - Glossary
    The Genomics Era: the Future of Genetics in Medicine - Glossary The glossary below provides a list of key terms used throughout the course. You do not need to read them all now; we’ll be linking back to the main glossary step wherever these terms appear, so you may refer back to this list if you are unsure of the terminology being used. Term Definition The process of matching reads back to their original Alignment position in the reference genome. An allele is one of a number of alternative forms of the same gene or genetic locus. We inherit one copy Allele of our genetic code from our mother and one copy of our genetic code from our father. Each copy is known as an allele. Microarray based genomic comparative hybridisation. This is a technique used to detect chromosome imbalances by comparing patient and control DNA and comparing differences between the two sets. It is Array CGH a useful technique for detecting small chromosome deletions and duplications which would not have been detected with more traditional karyotyping techniques. A unit of DNA. There are four bases which form the Base cross links (or rungs) of the DNA double helix: adenine (A), thymine (T), guanine (G) and cytosine (C). Capture see Target enrichment. The process by which a cell becomes specialized in Cell differentiation order to perform a specific function. Centromere The point at which the sister chromatids are joined. #1 FutureLearn A structure located in the nucleus all living cells, comprised of DNA bound around proteins called histones. The normal number of chromosomes in each Chromosome human cell nucleus is 46 and is composed of 22 pairs of autosomes and a pair of sex chromosomes which determine gender: males have an X and a Y chromosome whilst females have two X chromosomes.
    [Show full text]
  • Exome Sequencing in Early Disease Diagnosis
    Diabetes Updates Short Communication ISSN: 2631-5483 Exome Sequencing in early disease diagnosis: Are we on the right track? Musambil M* Department of Genetics, Strategic Center for Diabetes Research, King Saud University, Riyadh, Saudi Arabia Identification of genetic variants associated with monogenic to whole exome sequencing (WES) and targeted gene sequencing. syndromes, complex disorders and related traits opened up an avenue But WGS still remains very much expensive compared with WES and that had not been explored before, which is to translate the genetic targeted sequencing [6] (Tables 1 and 2). screening information into disease predicting tools which could WES could be briefly explained as the process of sequencing provide more efficient management of the disease by improving risk exons or the protein-encoding parts of the genes which represent and its prediction capabilities. The methods to uncover the genetics of these complex disorders have evolved over time. The International the functional part of the genome. WES gives a clear picture of high HapMap Project, carried out as a part of the Human Genome Project, penetrance allelic variation and its relationship to disease phenotype was successful in providing information about more than one million [7]. As WES targets exons and with the knowledge that Mendelian or SNPs (Single Nucleotide Polymorphisms) across the human genome. partly Mendelian variations are mediated by non-synonymous, splice A revolution in SNP genotyping technology had occurred, making it site and frameshift variations, exomes remain the most ideal regions possible to genotype hundreds of thousands of SNPs, opening new to be screened in order to link genetic variation to health and disease.
    [Show full text]
  • Whole Exome Sequencing (WES)
    Whole Exome Sequencing (WES) Turn Around Time: 30 Days TEST METHODOLOGY CPT Codes: Proband – 81415, Family Member – 81416 DNA will be extracted from whole blood or Test Includes: DNA Extraction other specimen types. Extracted DNA is Library Prep quantified and sheared to the correct size. The Exome Capture sample then undergoes library preparation and Library QC the exome is captured. After quality assurance, Illumina Platform Sequencing the captured library is then subjected to next Data Analysis generation DNA sequencing on the Illumina Sanger Variant Confirmation (if requested) platform. The reads from this sequencing are Interpreted Clinical Report aligned to a reference sequence and variations from this reference are identified. The sequence variants are then loaded into a commercial software package that contains data sources and Expedited WES testing is available. algorithms allowing for the evaluation of whole Contact the lab for more information. exome sequencing variants for evolutionary conservation, predicted impact on protein TEST DESCRIPTION structure and function (including Polyphen2 (5) and SIFT (6)), ability to disrupt conserved Whole Exome Sequencing (WES) is used to detect variants in a patient’s exome splice sites, and presence in databases including in order to determine the role of genomic variants in disease outcomes. The OMIM, dbSNP, and HGMD (1,2,3). The exome is a little more than 1% of the genome that codes for protein. The patient’s software annotates variants with this data, exome will be sequenced to an average depth of 100X with a minimum depth of considering both the reference gene model and coverage of 85X. Over 97% of the exome will be sequenced to a depth of 10X.
    [Show full text]
  • Genomic Technologies for Cancer Research
    Genomic Technologies for Cancer Research www.illumina.com/applications/cancer.html Table of Contents I. Introduction: Genomic Technologies for Cancer Research 3 II. Approaches for Detecting Somatic Mutations 4 Targeted Sequencing Solutions for Somatic Mutation Detection 4 Exome Sequencing 4 Focused Sequencing Panels 4 Custom Targeted Sequencing 4 Whole-Genome Sequencing Solutions 4 Data Analysis Tools for Somatic Variant Detection 5 III. Evaluating Germline Mutations in Cancer 6 Targeted Sequencing to Detect Common Germline Mutations 7 Microarray-Based Approaches 7 IV. Structural Variant Detection in Cancer 7 DNA and RNA Sequencing for Translocation Detection 8 Copy Number Variation Arrays 8 V. Investigating Gene Regulation in Cancer 8 DNA–Protein Interactions 8 DNA Methylation 9 RNA Sequencing 9 Targeted RNA Sequencing 9 Small RNA Sequencing 10 Data Analysis Tools for the Study of Gene Regulation 11 VI. Summary 11 For Research Use Only. Not for use in diagnostic procedures. I. Introduction: Genomic Technologies for Cancer Research In recent years, genomic technologies have emerged as invaluable tools in cancer research (Figure 1). International projects such as the International Cancer Genome Consortium (ICGC)1 and The Cancer Genome Atlas (TCGA)2, tasked with mapping the biology of dozens of tumor types, would not have been possible without these tools. Next-generation sequencing (NGS) and high-density microarrays are used to study the biology of cancer. Both provide the cancer research community with a growing body of knowledge that may lead to more effective drug design, better patient treatment options, and more accurate prognoses.3 Normal Neoplastic Changes Tumor Treatment Response Recurrence PROGRESSION Somatic Mutations Germline Gene Expression & Mutations Epigenetic Changes Additional Mutations Chromosomal Abnormalities HETEROGENEITY Figure 1: The Tumor Progression Pathway—Genomic technologies are helping researchers achieve a deeper understanding of the tumor progression pathway.
    [Show full text]
  • RNA Next Generation Sequencing Resources Available at the Experimental and Computational Genomics Core (ECGC)
    RNA next generation sequencing resources available at the Experimental and Computational Genomics Core (ECGC) Kornel Schuebel, PhD ECGC Resource Director [email protected] Telephone 410-614-0445 CRB2 Rm 131 (lab) CRB2 Rm 1m44 (office) What’s our mission? To facilitate easy access to genomic technologies and bioinformatics expertise, including experimental design, sample processing, and data analysis. To build educational and training opportunities for genomics analysis. Next Generation Microarray Sequencing Experimental and Computational Genomics Biostatistics and Genomics Bioinformatics Education Analysis The ECGC team Faculty Directors Staff Leslie Cope Michael Considine Sarah Wheelan Anuj Gupta Vasan Yegnasubramanian Jennifer Meyers Alyza Skaist Resource Director Hai Xu Kornel Schuebel Coordinators Lauren Ciotti Luda Danilova Daniel Vellucci Core faculty IT support Rob Scharpf Greg Smith Elana Fertig Dominic King How do I start my project? Let us know a little (a Lauren Sarah sentence or two is fine) Vasan Contact us at ecgc.jhmi.edu about your project Leslie Kornel Schedule and attend a consultation Lauren Meet with us to establish an Sarah experimental plan, discuss Vasan costs and a timeline Leslie Set up an iLab project report Kornel and drop off samples Drop off times are generally Lauren on Tuesdays and Thursdays Jennifer Kornel We will contact you to verify types of data analysis you want Anuj, Alyza, Michael Schedule a meeting to look We confirm with you the Sarah comparisons your iLabs Vasan at the data together report showed
    [Show full text]
  • Guide to Interpreting Genomic Reports: a Genomics Toolkit
    Guide to Interpreting Genomic Reports: A Genomics Toolkit A guide to genomic test results for non-genetics providers Created by the Practitioner Education Working Group of the Clinical Sequencing Exploratory Research (CSER) Consortium Genomic Report Toolkit Authors Kelly East, MS, CGC, Wendy Chung MD, PhD, Kate Foreman, MS, CGC, Mari Gilmore, MS, CGC, Michele Gornick, PhD, Lucia Hindorff, PhD, Tia Kauffman, MPH, Donna Messersmith , PhD, Cindy Prows, MSN, APRN, CNS, Elena Stoffel, MD, Joon-Ho Yu, MPh, PhD and Sharon Plon, MD, PhD About this resource This resource was created by a team of genomic testing experts. It is designed to help non-geneticist healthcare providers to understand genomic medicine and genome sequencing. The CSER Consortium1 is an NIH-funded group exploring genomic testing in clinical settings. Acknowledgements This work was conducted as part of the Clinical Sequencing Exploratory Research (CSER) Consortium, grants U01 HG006485, U01 HG006485, U01 HG006546, U01 HG006492, UM1 HG007301, UM1 HG007292, UM1 HG006508, U01 HG006487, U01 HG006507, R01 HG006618, and U01 HG007307. Special thanks to Alexandria Wyatt and Hugo O’Campo for graphic design and layout, Jill Pope for technical editing, and the entire CSER Practitioner Education Working Group for their time, energy, and support in developing this resource. Contents 1 Introduction and Overview ................................................................ 3 2 Diagnostic Results Related to Patient Symptoms: Pathogenic and Likely Pathogenic Variants . 8 3 Uncertain Results
    [Show full text]